ArticleJournal of chemical information and modeling2025
Test-Time Training Scaling Laws for Chemical Exploration in Drug Design.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- ChemFM as a scaling law guided foundation model pre-trained on informative chemicals.Communications chemistry · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
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Authors and funding
3 authors.
Funding
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Abstract
Chemical language models (CLMs) leveraging reinforcement learning (RL) have shown promise in de novo molecular design, yet often suffer from mode collapse, limiting their exploration capabilities. Inspired by test-time training (TTT) in large language models, we propose scaling TTT for CLMs to enhance chemical space exploration. We introduce MolExp, a novel benchmark emphasizing the discovery of structurally diverse molecules with similar bioactivity, simulating real-world drug design challenges. Our results demonstrate that scaling TTT by increasing the number of independent RL agents follows a log-linear scaling law, significantly improving exploration efficiency as measured by MolExp. In contrast, increasing TTT training time yields diminishing returns, even with exploration bonuses. We further evaluate cooperative RL strategies to enhance exploration efficiency. These findings provide a scalable framework for generative molecular design, offering insights into optimizing AI-driven drug discovery.
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Registered trials
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